factory_task_nut_bolt_pick.py

# Copyright (c) 2018-2023, NVIDIA Corporation
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"""Factory: Class for nut-bolt pick task.

Inherits nut-bolt environment class and abstract task class (not enforced). Can be executed with
PYTHON_PATH omniisaacgymenvs/scripts/rlgames_train.py task=FactoryTaskNutBoltPick
"""

import asyncio

import hydra
import omegaconf
import torch
import omni.kit
from omni.isaac.core.simulation_context import SimulationContext
from omni.isaac.core.utils.torch.transformations import tf_combine
from typing import Tuple

import omni.isaac.core.utils.torch as torch_utils
import omniisaacgymenvs.tasks.factory.factory_control as fc
from omniisaacgymenvs.tasks.factory.factory_env_nut_bolt import FactoryEnvNutBolt
from omniisaacgymenvs.tasks.factory.factory_schema_class_task import FactoryABCTask
from omniisaacgymenvs.tasks.factory.factory_schema_config_task import (
    FactorySchemaConfigTask,
)


class FactoryTaskNutBoltPick(FactoryEnvNutBolt, FactoryABCTask):
    def __init__(self, name, sim_config, env, offset=None) -> None:
        """Initialize environment superclass. Initialize instance variables."""

        super().__init__(name, sim_config, env)

        self._get_task_yaml_params()

    def _get_task_yaml_params(self) -> None:
        """Initialize instance variables from YAML files."""

        cs = hydra.core.config_store.ConfigStore.instance()
        cs.store(name="factory_schema_config_task", node=FactorySchemaConfigTask)

        self.cfg_task = omegaconf.OmegaConf.create(self._task_cfg)
        self.max_episode_length = (
            self.cfg_task.rl.max_episode_length
        )  # required instance var for VecTask

        asset_info_path = "../tasks/factory/yaml/factory_asset_info_nut_bolt.yaml"  # relative to Gym's Hydra search path (cfg dir)
        self.asset_info_nut_bolt = hydra.compose(config_name=asset_info_path)
        self.asset_info_nut_bolt = self.asset_info_nut_bolt[""][""][""]["tasks"][
            "factory"
        ][
            "yaml"
        ]  # strip superfluous nesting

        ppo_path = "train/FactoryTaskNutBoltPickPPO.yaml"  # relative to Gym's Hydra search path (cfg dir)
        self.cfg_ppo = hydra.compose(config_name=ppo_path)
        self.cfg_ppo = self.cfg_ppo["train"]  # strip superfluous nesting

    def post_reset(self) -> None:
        """Reset the world. Called only once, before simulation begins."""

        if self.cfg_task.sim.disable_gravity:
            self.disable_gravity()

        self.acquire_base_tensors()
        self._acquire_task_tensors()

        self.refresh_base_tensors()
        self.refresh_env_tensors()
        self._refresh_task_tensors()

        # Reset all envs
        indices = torch.arange(self._num_envs, dtype=torch.int64, device=self._device)
        asyncio.ensure_future(
            self.reset_idx_async(indices, randomize_gripper_pose=False)
        )

    def _acquire_task_tensors(self) -> None:
        """Acquire tensors."""

        # Grasp pose tensors
        nut_grasp_heights = self.bolt_head_heights + self.nut_heights * 0.5  # nut COM
        self.nut_grasp_pos_local = nut_grasp_heights * torch.tensor(
            [0.0, 0.0, 1.0], device=self.device
        ).repeat((self.num_envs, 1))
        self.nut_grasp_quat_local = (
            torch.tensor([0.0, 0.0, 1.0, 0.0], device=self.device)
            .unsqueeze(0)
            .repeat(self.num_envs, 1)
        )

        # Keypoint tensors
        self.keypoint_offsets = (
            self._get_keypoint_offsets(self.cfg_task.rl.num_keypoints)
            * self.cfg_task.rl.keypoint_scale
        )
        self.keypoints_gripper = torch.zeros(
            (self.num_envs, self.cfg_task.rl.num_keypoints, 3),
            dtype=torch.float32,
            device=self.device,
        )
        self.keypoints_nut = torch.zeros_like(
            self.keypoints_gripper, device=self.device
        )
        self.identity_quat = (
            torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device)
            .unsqueeze(0)
            .repeat(self.num_envs, 1)
        )

        self.actions = torch.zeros(
            (self.num_envs, self.num_actions), device=self.device
        )

    def pre_physics_step(self, actions) -> None:
        """Reset environments. Apply actions from policy. Simulation step called after this method."""

        if not self.world.is_playing():
            return

        env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
        if len(env_ids) > 0:
            self.reset_idx(env_ids, randomize_gripper_pose=True)

        self.actions = actions.clone().to(
            self.device
        )  # shape = (num_envs, num_actions); values = [-1, 1]

        self._apply_actions_as_ctrl_targets(
            actions=self.actions,
            ctrl_target_gripper_dof_pos=self.asset_info_franka_table.franka_gripper_width_max,
            do_scale=True,
        )

    async def pre_physics_step_async(self, actions) -> None:
        """Reset environments. Apply actions from policy. Simulation step called after this method."""

        if not self.world.is_playing():
            return

        env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
        if len(env_ids) > 0:
            await self.reset_idx_async(env_ids, randomize_gripper_pose=True)

        self.actions = actions.clone().to(
            self.device
        )  # shape = (num_envs, num_actions); values = [-1, 1]

        self._apply_actions_as_ctrl_targets(
            actions=self.actions,
            ctrl_target_gripper_dof_pos=self.asset_info_franka_table.franka_gripper_width_max,
            do_scale=True,
        )

    def reset_idx(self, env_ids, randomize_gripper_pose) -> None:
        """Reset specified environments."""

        self._reset_franka(env_ids)
        self._reset_object(env_ids)

        if randomize_gripper_pose:
            self._randomize_gripper_pose(
                env_ids, sim_steps=self.cfg_task.env.num_gripper_move_sim_steps
            )

        self._reset_buffers(env_ids)

    async def reset_idx_async(self, env_ids, randomize_gripper_pose) -> None:
        """Reset specified environments."""

        self._reset_franka(env_ids)
        self._reset_object(env_ids)

        if randomize_gripper_pose:
            await self._randomize_gripper_pose_async(
                env_ids, sim_steps=self.cfg_task.env.num_gripper_move_sim_steps
            )

        self._reset_buffers(env_ids)

    def _reset_franka(self, env_ids) -> None:
        """Reset DOF states and DOF targets of Franka."""

        self.dof_pos[env_ids] = torch.cat(
            (
                torch.tensor(
                    self.cfg_task.randomize.franka_arm_initial_dof_pos,
                    device=self.device,
                ),
                torch.tensor(
                    [self.asset_info_franka_table.franka_gripper_width_max],
                    device=self.device,
                ),
                torch.tensor(
                    [self.asset_info_franka_table.franka_gripper_width_max],
                    device=self.device,
                ),
            ),
            dim=-1,
        )  # shape = (num_envs, num_dofs)
        self.dof_vel[env_ids] = 0.0  # shape = (num_envs, num_dofs)
        self.ctrl_target_dof_pos[env_ids] = self.dof_pos[env_ids]

        indices = env_ids.to(dtype=torch.int32)
        self.frankas.set_joint_positions(self.dof_pos[env_ids], indices=indices)
        self.frankas.set_joint_velocities(self.dof_vel[env_ids], indices=indices)

    def _reset_object(self, env_ids) -> None:
        """Reset root states of nut and bolt."""

        # Randomize root state of nut
        nut_noise_xy = 2 * (
            torch.rand((self.num_envs, 2), dtype=torch.float32, device=self.device)
            - 0.5
        )  # [-1, 1]
        nut_noise_xy = nut_noise_xy @ torch.diag(
            torch.tensor(self.cfg_task.randomize.nut_pos_xy_noise, device=self.device)
        )

        self.nut_pos[env_ids, 0] = (
            self.cfg_task.randomize.nut_pos_xy_initial[0] + nut_noise_xy[env_ids, 0]
        )
        self.nut_pos[env_ids, 1] = (
            self.cfg_task.randomize.nut_pos_xy_initial[1] + nut_noise_xy[env_ids, 1]
        )
        self.nut_pos[
            env_ids, 2
        ] = self.cfg_base.env.table_height - self.bolt_head_heights.squeeze(-1)

        self.nut_quat[env_ids, :] = torch.tensor(
            [1.0, 0.0, 0.0, 0.0], dtype=torch.float32, device=self.device
        ).repeat(len(env_ids), 1)

        self.nut_linvel[env_ids, :] = 0.0
        self.nut_angvel[env_ids, :] = 0.0

        indices = env_ids.to(dtype=torch.int32)
        self.nuts.set_world_poses(
            self.nut_pos[env_ids] + self.env_pos[env_ids],
            self.nut_quat[env_ids],
            indices,
        )
        self.nuts.set_velocities(
            torch.cat((self.nut_linvel[env_ids], self.nut_angvel[env_ids]), dim=1),
            indices,
        )

        # Randomize root state of bolt
        bolt_noise_xy = 2 * (
            torch.rand((self.num_envs, 2), dtype=torch.float32, device=self.device)
            - 0.5
        )  # [-1, 1]
        bolt_noise_xy = bolt_noise_xy @ torch.diag(
            torch.tensor(self.cfg_task.randomize.bolt_pos_xy_noise, device=self.device)
        )

        self.bolt_pos[env_ids, 0] = (
            self.cfg_task.randomize.bolt_pos_xy_initial[0] + bolt_noise_xy[env_ids, 0]
        )
        self.bolt_pos[env_ids, 1] = (
            self.cfg_task.randomize.bolt_pos_xy_initial[1] + bolt_noise_xy[env_ids, 1]
        )
        self.bolt_pos[env_ids, 2] = self.cfg_base.env.table_height

        self.bolt_quat[env_ids, :] = torch.tensor(
            [1.0, 0.0, 0.0, 0.0], dtype=torch.float32, device=self.device
        ).repeat(len(env_ids), 1)

        indices = env_ids.to(dtype=torch.int32)
        self.bolts.set_world_poses(
            self.bolt_pos[env_ids] + self.env_pos[env_ids],
            self.bolt_quat[env_ids],
            indices,
        )

    def _reset_buffers(self, env_ids) -> None:
        """Reset buffers."""

        self.reset_buf[env_ids] = 0
        self.progress_buf[env_ids] = 0

    def _apply_actions_as_ctrl_targets(
        self, actions, ctrl_target_gripper_dof_pos, do_scale
    ) -> None:
        """Apply actions from policy as position/rotation/force/torque targets."""

        # Interpret actions as target pos displacements and set pos target
        pos_actions = actions[:, 0:3]
        if do_scale:
            pos_actions = pos_actions @ torch.diag(
                torch.tensor(self.cfg_task.rl.pos_action_scale, device=self.device)
            )
        self.ctrl_target_fingertip_midpoint_pos = (
            self.fingertip_midpoint_pos + pos_actions
        )

        # Interpret actions as target rot (axis-angle) displacements
        rot_actions = actions[:, 3:6]
        if do_scale:
            rot_actions = rot_actions @ torch.diag(
                torch.tensor(self.cfg_task.rl.rot_action_scale, device=self.device)
            )

        # Convert to quat and set rot target
        angle = torch.norm(rot_actions, p=2, dim=-1)
        axis = rot_actions / angle.unsqueeze(-1)
        rot_actions_quat = torch_utils.quat_from_angle_axis(angle, axis)
        if self.cfg_task.rl.clamp_rot:
            rot_actions_quat = torch.where(
                angle.unsqueeze(-1).repeat(1, 4) > self.cfg_task.rl.clamp_rot_thresh,
                rot_actions_quat,
                torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).repeat(
                    self.num_envs, 1
                ),
            )
        self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_mul(
            rot_actions_quat, self.fingertip_midpoint_quat
        )

        if self.cfg_ctrl["do_force_ctrl"]:
            # Interpret actions as target forces and target torques
            force_actions = actions[:, 6:9]
            if do_scale:
                force_actions = force_actions @ torch.diag(
                    torch.tensor(
                        self.cfg_task.rl.force_action_scale, device=self.device
                    )
                )

            torque_actions = actions[:, 9:12]
            if do_scale:
                torque_actions = torque_actions @ torch.diag(
                    torch.tensor(
                        self.cfg_task.rl.torque_action_scale, device=self.device
                    )
                )

            self.ctrl_target_fingertip_contact_wrench = torch.cat(
                (force_actions, torque_actions), dim=-1
            )

        self.ctrl_target_gripper_dof_pos = ctrl_target_gripper_dof_pos

        self.generate_ctrl_signals()

    def post_physics_step(
        self,
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
        """Step buffers. Refresh tensors. Compute observations and reward. Reset environments."""

        self.progress_buf[:] += 1

        if self.world.is_playing():
            # In this policy, episode length is constant
            is_last_step = self.progress_buf[0] == self.max_episode_length - 1
            if is_last_step:
                # At this point, robot has executed RL policy. Now close gripper and lift (open-loop)
                if self.cfg_task.env.close_and_lift:
                    self._close_gripper(
                        sim_steps=self.cfg_task.env.num_gripper_close_sim_steps
                    )
                    self._lift_gripper(
                        franka_gripper_width=0.0,
                        lift_distance=0.3,
                        sim_steps=self.cfg_task.env.num_gripper_lift_sim_steps,
                    )

            self.refresh_base_tensors()
            self.refresh_env_tensors()
            self._refresh_task_tensors()
            self.get_observations()
            self.get_states()
            self.calculate_metrics()
            self.get_extras()

        return self.obs_buf, self.rew_buf, self.reset_buf, self.extras

    async def post_physics_step_async(self):
        """Step buffers. Refresh tensors. Compute observations and reward. Reset environments."""

        self.progress_buf[:] += 1

        if self.world.is_playing():
            # In this policy, episode length is constant
            is_last_step = self.progress_buf[0] == self.max_episode_length - 1

            if self.cfg_task.env.close_and_lift:
                # At this point, robot has executed RL policy. Now close gripper and lift (open-loop)
                if is_last_step:
                    await self._close_gripper_async(
                        sim_steps=self.cfg_task.env.num_gripper_close_sim_steps
                    )
                    await self._lift_gripper_async(
                        sim_steps=self.cfg_task.env.num_gripper_lift_sim_steps
                    )

            self.refresh_base_tensors()
            self.refresh_env_tensors()
            self._refresh_task_tensors()
            self.get_observations()
            self.get_states()
            self.calculate_metrics()
            self.get_extras()

        return self.obs_buf, self.rew_buf, self.reset_buf, self.extras

    def _refresh_task_tensors(self):
        """Refresh tensors."""

        # Compute pose of nut grasping frame
        self.nut_grasp_quat, self.nut_grasp_pos = tf_combine(
            self.nut_quat,
            self.nut_pos,
            self.nut_grasp_quat_local,
            self.nut_grasp_pos_local,
        )

        # Compute pos of keypoints on gripper and nut in world frame
        for idx, keypoint_offset in enumerate(self.keypoint_offsets):
            self.keypoints_gripper[:, idx] = tf_combine(
                self.fingertip_midpoint_quat,
                self.fingertip_midpoint_pos,
                self.identity_quat,
                keypoint_offset.repeat(self.num_envs, 1),
            )[1]
            self.keypoints_nut[:, idx] = tf_combine(
                self.nut_grasp_quat,
                self.nut_grasp_pos,
                self.identity_quat,
                keypoint_offset.repeat(self.num_envs, 1),
            )[1]

    def get_observations(self) -> dict:
        """Compute observations."""

        # Shallow copies of tensors
        obs_tensors = [
            self.fingertip_midpoint_pos,
            self.fingertip_midpoint_quat,
            self.fingertip_midpoint_linvel,
            self.fingertip_midpoint_angvel,
            self.nut_grasp_pos,
            self.nut_grasp_quat,
        ]

        self.obs_buf = torch.cat(
            obs_tensors, dim=-1
        )  # shape = (num_envs, num_observations)

        observations = {self.frankas.name: {"obs_buf": self.obs_buf}}

        return observations

    def calculate_metrics(self) -> None:
        """Update reward and reset buffers."""

        self._update_reset_buf()
        self._update_rew_buf()

    def _update_reset_buf(self) -> None:
        """Assign environments for reset if successful or failed."""

        # If max episode length has been reached
        self.reset_buf[:] = torch.where(
            self.progress_buf[:] >= self.max_episode_length - 1,
            torch.ones_like(self.reset_buf),
            self.reset_buf,
        )

    def _update_rew_buf(self) -> None:
        """Compute reward at current timestep."""

        keypoint_reward = -self._get_keypoint_dist()
        action_penalty = (
            torch.norm(self.actions, p=2, dim=-1)
            * self.cfg_task.rl.action_penalty_scale
        )

        self.rew_buf[:] = (
            keypoint_reward * self.cfg_task.rl.keypoint_reward_scale
            - action_penalty * self.cfg_task.rl.action_penalty_scale
        )

        # In this policy, episode length is constant across all envs
        is_last_step = self.progress_buf[0] == self.max_episode_length - 1

        if is_last_step:
            # Check if nut is picked up and above table
            lift_success = self._check_lift_success(height_multiple=3.0)
            self.rew_buf[:] += lift_success * self.cfg_task.rl.success_bonus
            self.extras["successes"] = torch.mean(lift_success.float())

    def _get_keypoint_offsets(self, num_keypoints) -> torch.Tensor:
        """Get uniformly-spaced keypoints along a line of unit length, centered at 0."""

        keypoint_offsets = torch.zeros((num_keypoints, 3), device=self.device)
        keypoint_offsets[:, -1] = (
            torch.linspace(0.0, 1.0, num_keypoints, device=self.device) - 0.5
        )

        return keypoint_offsets

    def _get_keypoint_dist(self) -> torch.Tensor:
        """Get keypoint distance."""

        keypoint_dist = torch.sum(
            torch.norm(self.keypoints_nut - self.keypoints_gripper, p=2, dim=-1), dim=-1
        )

        return keypoint_dist

    def _close_gripper(self, sim_steps=20) -> None:
        """Fully close gripper using controller. Called outside RL loop (i.e., after last step of episode)."""

        self._move_gripper_to_dof_pos(gripper_dof_pos=0.0, sim_steps=sim_steps)

    def _move_gripper_to_dof_pos(self, gripper_dof_pos, sim_steps=20) -> None:
        """Move gripper fingers to specified DOF position using controller."""

        delta_hand_pose = torch.zeros(
            (self.num_envs, 6), device=self.device
        )  # No hand motion
        self._apply_actions_as_ctrl_targets(
            delta_hand_pose, gripper_dof_pos, do_scale=False
        )

        # Step sim
        for _ in range(sim_steps):
            SimulationContext.step(self.world, render=True)

    def _lift_gripper(
        self, franka_gripper_width=0.0, lift_distance=0.3, sim_steps=20
    ) -> None:
        """Lift gripper by specified distance. Called outside RL loop (i.e., after last step of episode)."""

        delta_hand_pose = torch.zeros([self.num_envs, 6], device=self.device)
        delta_hand_pose[:, 2] = lift_distance

        # Step sim
        for _ in range(sim_steps):
            self._apply_actions_as_ctrl_targets(
                delta_hand_pose, franka_gripper_width, do_scale=False
            )
            SimulationContext.step(self.world, render=True)

    async def _close_gripper_async(self, sim_steps=20) -> None:
        """Fully close gripper using controller. Called outside RL loop (i.e., after last step of episode)."""
        await self._move_gripper_to_dof_pos_async(
            gripper_dof_pos=0.0, sim_steps=sim_steps
        )

    async def _move_gripper_to_dof_pos_async(
        self, gripper_dof_pos, sim_steps=20
    ) -> None:
        """Move gripper fingers to specified DOF position using controller."""

        delta_hand_pose = torch.zeros(
            (self.num_envs, self.cfg_task.env.numActions), device=self.device
        )  # No hand motion
        self._apply_actions_as_ctrl_targets(
            delta_hand_pose, gripper_dof_pos, do_scale=False
        )

        # Step sim
        for _ in range(sim_steps):
            await omni.kit.app.get_app().next_update_async()

    async def _lift_gripper_async(
        self, franka_gripper_width=0.0, lift_distance=0.3, sim_steps=20
    ) -> None:
        """Lift gripper by specified distance. Called outside RL loop (i.e., after last step of episode)."""

        delta_hand_pose = torch.zeros([self.num_envs, 6], device=self.device)
        delta_hand_pose[:, 2] = lift_distance

        # Step sim
        for _ in range(sim_steps):
            self._apply_actions_as_ctrl_targets(
                delta_hand_pose, franka_gripper_width, do_scale=False
            )
            await omni.kit.app.get_app().next_update_async()

    def _check_lift_success(self, height_multiple) -> torch.Tensor:
        """Check if nut is above table by more than specified multiple times height of nut."""

        lift_success = torch.where(
            self.nut_pos[:, 2]
            > self.cfg_base.env.table_height
            + self.nut_heights.squeeze(-1) * height_multiple,
            torch.ones((self.num_envs,), device=self.device),
            torch.zeros((self.num_envs,), device=self.device),
        )

        return lift_success

    def _randomize_gripper_pose(self, env_ids, sim_steps) -> None:
        """Move gripper to random pose."""

        # step once to update physx with the newly set joint positions from reset_franka()
        SimulationContext.step(self.world, render=True)

        # Set target pos above table
        self.ctrl_target_fingertip_midpoint_pos = torch.tensor(
            [0.0, 0.0, self.cfg_base.env.table_height], device=self.device
        ) + torch.tensor(
            self.cfg_task.randomize.fingertip_midpoint_pos_initial, device=self.device
        )
        self.ctrl_target_fingertip_midpoint_pos = (
            self.ctrl_target_fingertip_midpoint_pos.unsqueeze(0).repeat(
                self.num_envs, 1
            )
        )

        fingertip_midpoint_pos_noise = 2 * (
            torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device)
            - 0.5
        )  # [-1, 1]
        fingertip_midpoint_pos_noise = fingertip_midpoint_pos_noise @ torch.diag(
            torch.tensor(
                self.cfg_task.randomize.fingertip_midpoint_pos_noise, device=self.device
            )
        )
        self.ctrl_target_fingertip_midpoint_pos += fingertip_midpoint_pos_noise

        # Set target rot
        ctrl_target_fingertip_midpoint_euler = (
            torch.tensor(
                self.cfg_task.randomize.fingertip_midpoint_rot_initial,
                device=self.device,
            )
            .unsqueeze(0)
            .repeat(self.num_envs, 1)
        )

        fingertip_midpoint_rot_noise = 2 * (
            torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device)
            - 0.5
        )  # [-1, 1]
        fingertip_midpoint_rot_noise = fingertip_midpoint_rot_noise @ torch.diag(
            torch.tensor(
                self.cfg_task.randomize.fingertip_midpoint_rot_noise, device=self.device
            )
        )
        ctrl_target_fingertip_midpoint_euler += fingertip_midpoint_rot_noise
        self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_from_euler_xyz(
            ctrl_target_fingertip_midpoint_euler[:, 0],
            ctrl_target_fingertip_midpoint_euler[:, 1],
            ctrl_target_fingertip_midpoint_euler[:, 2],
        )

        # Step sim and render
        for _ in range(sim_steps):
            if not self.world.is_playing():
                return

            self.refresh_base_tensors()
            self.refresh_env_tensors()
            self._refresh_task_tensors()

            pos_error, axis_angle_error = fc.get_pose_error(
                fingertip_midpoint_pos=self.fingertip_midpoint_pos,
                fingertip_midpoint_quat=self.fingertip_midpoint_quat,
                ctrl_target_fingertip_midpoint_pos=self.ctrl_target_fingertip_midpoint_pos,
                ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_midpoint_quat,
                jacobian_type=self.cfg_ctrl["jacobian_type"],
                rot_error_type="axis_angle",
            )

            delta_hand_pose = torch.cat((pos_error, axis_angle_error), dim=-1)
            actions = torch.zeros(
                (self.num_envs, self.cfg_task.env.numActions), device=self.device
            )
            actions[:, :6] = delta_hand_pose

            self._apply_actions_as_ctrl_targets(
                actions=actions,
                ctrl_target_gripper_dof_pos=self.asset_info_franka_table.franka_gripper_width_max,
                do_scale=False,
            )

            SimulationContext.step(self.world, render=True)

        self.dof_vel[env_ids, :] = torch.zeros_like(self.dof_vel[env_ids])

        indices = env_ids.to(dtype=torch.int32)
        self.frankas.set_joint_velocities(self.dof_vel[env_ids], indices=indices)

        # step once to update physx with the newly set joint velocities
        SimulationContext.step(self.world, render=True)

    async def _randomize_gripper_pose_async(self, env_ids, sim_steps) -> None:
        """Move gripper to random pose."""

        # step once to update physx with the newly set joint positions from reset_franka()
        await omni.kit.app.get_app().next_update_async()

        # Set target pos above table
        self.ctrl_target_fingertip_midpoint_pos = torch.tensor(
            [0.0, 0.0, self.cfg_base.env.table_height], device=self.device
        ) + torch.tensor(
            self.cfg_task.randomize.fingertip_midpoint_pos_initial, device=self.device
        )
        self.ctrl_target_fingertip_midpoint_pos = (
            self.ctrl_target_fingertip_midpoint_pos.unsqueeze(0).repeat(
                self.num_envs, 1
            )
        )

        fingertip_midpoint_pos_noise = 2 * (
            torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device)
            - 0.5
        )  # [-1, 1]
        fingertip_midpoint_pos_noise = fingertip_midpoint_pos_noise @ torch.diag(
            torch.tensor(
                self.cfg_task.randomize.fingertip_midpoint_pos_noise, device=self.device
            )
        )
        self.ctrl_target_fingertip_midpoint_pos += fingertip_midpoint_pos_noise

        # Set target rot
        ctrl_target_fingertip_midpoint_euler = (
            torch.tensor(
                self.cfg_task.randomize.fingertip_midpoint_rot_initial,
                device=self.device,
            )
            .unsqueeze(0)
            .repeat(self.num_envs, 1)
        )
        fingertip_midpoint_rot_noise = 2 * (
            torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device)
            - 0.5
        )  # [-1, 1]
        fingertip_midpoint_rot_noise = fingertip_midpoint_rot_noise @ torch.diag(
            torch.tensor(
                self.cfg_task.randomize.fingertip_midpoint_rot_noise, device=self.device
            )
        )
        ctrl_target_fingertip_midpoint_euler += fingertip_midpoint_rot_noise
        self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_from_euler_xyz(
            ctrl_target_fingertip_midpoint_euler[:, 0],
            ctrl_target_fingertip_midpoint_euler[:, 1],
            ctrl_target_fingertip_midpoint_euler[:, 2],
        )

        # Step sim and render
        for _ in range(sim_steps):
            self.refresh_base_tensors()
            self.refresh_env_tensors()
            self._refresh_task_tensors()

            pos_error, axis_angle_error = fc.get_pose_error(
                fingertip_midpoint_pos=self.fingertip_midpoint_pos,
                fingertip_midpoint_quat=self.fingertip_midpoint_quat,
                ctrl_target_fingertip_midpoint_pos=self.ctrl_target_fingertip_midpoint_pos,
                ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_midpoint_quat,
                jacobian_type=self.cfg_ctrl["jacobian_type"],
                rot_error_type="axis_angle",
            )

            delta_hand_pose = torch.cat((pos_error, axis_angle_error), dim=-1)
            actions = torch.zeros(
                (self.num_envs, self.cfg_task.env.numActions), device=self.device
            )
            actions[:, :6] = delta_hand_pose

            self._apply_actions_as_ctrl_targets(
                actions=actions,
                ctrl_target_gripper_dof_pos=self.asset_info_franka_table.franka_gripper_width_max,
                do_scale=False,
            )

            await omni.kit.app.get_app().next_update_async()

        self.dof_vel[env_ids, :] = torch.zeros_like(self.dof_vel[env_ids])

        indices = env_ids.to(dtype=torch.int32)
        self.frankas.set_joint_velocities(self.dof_vel[env_ids], indices=indices)

        # step once to update physx with the newly set joint velocities
        await omni.kit.app.get_app().next_update_async()